It is well known that electrical currents are associated with brain activity. These electrical currents are produced by neurons in the brain and are referred to as “neural currents.” Information about neural currents provides a means for understanding brain function. For example, the performance of a specific motor function will involve particular areas of the brain, and the dynamic behavior of the neural currents associated with the motor function provides information about the sequence in which different areas of the brain are involved.
Electroencephalography (“EEG”) is a technique that may be used for measuring neural currents. In EEG, electrodes are placed at various locations on the scalp and the difference between the electrical potential at one location and another is measured. If a large number of neurons are concurrently active, it is possible to detect the resulting neural currents. An advantage of EEG is that it has high temporal resolution, i.e., the technique detects the presence of a neural current with only a slight delay from the time when the neurons were active. Because of its high temporal resolution, EEG provides important information about the timing of brain functions. However, a disadvantage of EEG is that it has limited spatial resolution, i.e., it is unable to identify with great accuracy the spatial location of the sources of electrical activity within the brain.
Magnetoencephalography (“MEG”) may also be used for detecting neural currents, i.e., by responding to the magnetic fields that are generated thereby. In MEG, the magnetic flux emanating from voltage sources in the brain induces a current in coils that surround the head. The induced current is used to create an image of brain activity. MEG is similar to EEG in that it has the advantage of high temporal resolution and the disadvantage of limited spatial resolution. In addition, the magnetic flux from neural currents is very small relative to background magnetic noise. Therefore, the MEG device must be very sensitive to small magnetic signals, and must be shielded from the background magnetic noise to discern the desired signals. Such shielding is costly to provide, and MEG cannot be used simultaneously with other techniques producing electromagnetic radiations.
Magnetic resonance imaging (“MRI”) is most often used for measuring blood flow or blood oxygenation levels within the brain. The technique is based on the principles of nuclear magnetic resonance (“NMR”) and a brief explanation is necessary to understanding.
Most atomic nuclei possess a nonzero nuclear spin quantum number and a coaxial magnetic moment about a corresponding spin axis. The nuclei may be characterized as magnetized gyroscopes. Just as for the angular momentum vector of a gyroscope in a gravitational field, the vector moments of the nuclei precess about their spin axes in the presence of a magnetic field at a frequency (“Larmor frequency”) that is proportional to the magnetic moment of the nuclei multiplied by the magnitude of the magnetic field.
The vector moments of the precessing nuclei trace a cone in space describing an angle with respect to the direction of the magnetic field. Where there are many nuclei, such as in the body, the vectors sum to a single, ensemble moment aligned with the magnetic field, the lateral vector components averaging out.
In MRI, a large, static first magnetic field is applied to a body. This causes the ensemble moment of the nuclei in the body to align with the direction of the first magnetic field, as the nuclei precess at the Larmor frequency. In addition, a second, alternating magnetic field is applied to the body in a direction perpendicular to the direction of the first magnetic field. The frequency of the second magnetic field is adjusted to match the Larmor frequency. In that special circumstance, the nuclei precess about the direction of the second magnetic field as though the static field were absent. Thence, as a result of the application of the second magnetic field at the Larmor frequency, the ensemble moment tips away from being aligned with the first magnetic field.
The second magnetic field is produced as transmitted electromagnetic radiation at radio frequency (RF). It is typically provided as a pulse. The amount of tipping can be controlled by the duration of the pulse, it being desirable to rotate the ensemble moment from alignment with the static field π2 radians. When the pulse is turned off, the ensemble moment relaxes (“spin relaxation”), or loses energy, so that it re-aligns with the static field.
This relaxation occurs through two kinds of energy loss mechanisms: spin-spin interactions and spin-lattice interactions (where the term “lattice” is used loosely in the context of liquids or other noncrystalline environments). Because the energy states for the spin angular momenta are quantized, these interactions must permit precise amounts of energy loss or relaxation cannot occur, and due to the randomness of the interactions, it results that a substantial time is required for relaxation.
The rate of relaxation for a given atomic nucleus depends on magnetic field fluctuations caused by its neighbors as a result of thermal agitation. Particularly, spin-lattice interactions cause the energy provided by the RF pulse to decay exponentially with a time constant T1, and decay due to spin-spin interactions is described by an associated time constant T2. Where the fluctuations occur at rates that are either to large or too small compared to the Larmor frequency, energy dissipation is inefficient and therefore slow, resulting in long decay times T1 and T2.
In order to measure the times T, it is noted that energy lost in the transition of an atomic nucleus from a higher energy state (corresponding to precession due to the second, alternating magnetic field) to a lower energy state (corresponding to spin relaxation) produces radiation at the frequency defined by the energy difference between the energy states. For protons in body tissue, this radiation (termed “free induction signal”) is in the radio frequency range and is detected with a coil as an electrical signal indicative of the decay times T.
The RF pulse excite all the atomic nuclei at once. The free induction signal that follows has a highly complex time dependence. However, this complex decay waveform can be Fourier transformed to provide discernible NMR spectra indicative of the type and amount of atomic nuclei, as well as their atomic environment. From this spectroscopic information, tissue type can be determined.
Even so, the spectra do not contain any spatial information so that an image cannot yet be formed. To solve this problem, magnetic field gradient (“MFG”) pulses are used to spatially encode the free induction signals, to provide for locating the atomic nuclei responsible for the measured spectra in space. The MFG pulses are spatially varying magnetic fields generated by coils. The pulses are aligned with the first, static magnetic field and provide a linear gradient to the field along the x, y, or z axis. For a linear gradient in the z direction, for example, all points on the x and y axes will see the same magnetic field, providing a planar “slice” of data corresponding to that value of the first magnetic field. Where the first magnetic field is altered due to the gradient, the Larmor frequency is altered, and there will not be resonance with the second, alternating magnetic field. To image another slice, the second, alternating magnetic field can be adjusted.
An advantage of MRI is that it provides images with good spatial resolution. A disadvantage of MRI is that the time required for spin relaxation, even the relatively short time required for relaxation of protons in lipids, limits the temporal resolution of MRI.
It has been proposed to directly measure neural currents using MRI. The proposed technique is referred to herein as Magnetic Resonance Neural Current Imaging (“MRNCI”). One method is based on the principle that a current-carrying conductor experiences a force (a Lorentz force) when it is placed in a magnetic field. The magnitude of the force is proportional to the amount of current and the strength of the magnetic field. The direction of the force is perpendicular to the direction of current flow. A conductor that is not rigidly confined will be displaced in the response to the Lorentz force.
Neural currents flow through nerve cells which are therefore current-carrying conductors. Accordingly, nerve cells may be displaced by applying a magnetic field in proportion to the amount of neural activity carried thereby. If this displacement is large enough, it may be seen using imaging techniques such as MRI. However, the amount of neural current is very small, so to obtain a detectable displacement requires a very large magnetic field MRI devices are capable of producing very large magnetic fields, and could also be used according to MRNCI to produce images before and during a period of neural activity. The images would be compared by subtraction to produce a new image showing tissue displacement. However, MRNCI has not yet been shown to be able to detect currents as small as neural currents. The magnitude of such currents is of the same order of magnitude as the noise limits of modem MRI systems. Thus, successful implementation of MRNCI as proposed will require increasing the sensitivity of MRI measurements.
Moreover, the time required for obtaining an MRI image is substantially greater than the time required for accurate characterization of neural activity. The spin relaxations occur over a period of time on the order of 50 ms, the entirety of which must be taken to obtain all the data corresponding to a single “snapshot” or image slice. On the other hand, neural currents may only exist for time periods on the order of 1 ms, so that the physical displacement of the current-carrying conductor ceases before the MRI has had sufficient opportunity to “see” it.
The problem of detecting and therefore locating sources of neural activity is a subset of the problem of locating remote sources of electrical activity generally. The sources responsible for producing, for example, EEG data, are generally inaccessible to being probed directly. Therefore, the location and characteristics of these sources must be inferred from the fields they produce outside the body. Deducing the source magnitudes and spatial coordinates from measurements on such fields is known as “solving the inverse problem,” i.e., reconstructing the sources and their distribution from the results that they are known to have produced.
For a limited number of sensors, the data produced thereby are inherently ambiguous, in that a number of different solutions to the inverse problem can fit the data. The number of possible solutions is reduced by using more sensors and by sensing with greater precision; however, there is a practical limit to improving resolution, and therefore limiting the number of potential solutions to the inverse problem by improved means for sensing alone.
Methods facilitating solving the inverse problem have been developed by the present inventor, along with others, which are described in U.S. Pat. No. 6,330,470. These methods not only sense the electromagnetic fields generated by sources within the body, but reciprocally stimulate them as well to produce additional information about their location. These methods may be anticipated to be useful for neural current analysis by providing an improved tool for solving the inverse problem, but solving this problem to a high degree of resolution for extremely weak sources is inherently difficult.
In a paper entitled “Bayesian Inference Applied to the Electromagnetic Inverse Problem,” D. M. Schmidt et al. have proposed a method for solving the inverse problem for sources of neural current. Basically, the proposed method begins by assigning probabilities to possible locations of neural current activity by using anatomical data, physiological data, or results from measurements such as MRI or PET (positron emission tomography). Rather than solving the inverse problem, therefore, the method provides an educated guess. For example, it may be surmised that particular neural activity would occur in the cortex, and the location of the cortex is known from anatomical data. Next, EEG (electroencephalograph) or MEG (magnetoencephalograph) data are acquired such as mentioned above. Finally, Bayesian statistics are used to estimate the post-acquisition probabilities for the possible locations of neural activity given their pre-acquisition probabilities as determined above. While some success for the method has been reported, neural current imaging remains inherently difficult due to the weakness of the sources and improved methods are being sought.
Accordingly, there is a need for a method for imaging neural currents that offers improved spatial and temporal resolution.